Some Practice for Improving the Search Results of E-commerce
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Abstract (arXiv)
In the Amazon KDD Cup 2022, we aim to apply natural language processing methods to improve the quality of search results that can significantly enhance user experience and engagement with search engines for e-commerce. We discuss our practical solution for this competition, ranking 6th in task one, 2nd in task two, and 2nd in task 3. The code is available at https://github.com/wufanyou/KDD-Cup-2022-Amazon.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 6 of its 9 references that are in the map. First posted on arXiv 30 Jul 2022.
Builds on
6 of 9 references.
References · 6
- 2015Distilling the Knowledge in a Neural Networkcited by 10,324
- 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
- 2020Big Bird: Transformers for Longer Sequencescited by 1,684
- 2021DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharingcited by 1,254
- 2021COCO-LM: Correcting and Contrasting Text Sequences for Language Model Pretrainingcited by 126
- 2022Shopping Queries Dataset: A Large-Scale ESCI Benchmark for Improving Product Searchcited by 65
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Citing papers per year: 2024: 1.
1 paper.